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Classifying Small Lesions on Breast MRI through Dynamic Enhancement Pattern Characterization

  • Mahesh B. Nagarajan
  • Markus B. Huber
  • Thomas Schlossbauer
  • Gerda Leinsinger
  • Andrzej Krol
  • Axel Wismüller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

Dynamic characterization of the lesion enhancement pattern can improve the classification performance of small diagnostically challenging lesions on dynamic-contrast enhanced MRI. This involves extraction of texture features from all post-contrast images of the lesion rather than using the first post-contrast image alone. In this study, statistical texture features derived from gray-level co-occurrence matrices are extracted from all five post-contrast images of 60 lesions and then used in a supervised learning task with a support vector regressor. Our results show that this approach significantly improves the performance of classifying small lesions (p < 0.05). This suggests that such dynamic characterization of lesion enhancement has significant potential in assisting breast cancer diagnosis for small lesions.

Keywords

dynamic breast MRI texture analysis dynamic enhancement characterization gray-level co-occurence matrix support vector regression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mahesh B. Nagarajan
    • 1
  • Markus B. Huber
    • 1
  • Thomas Schlossbauer
    • 2
  • Gerda Leinsinger
    • 2
  • Andrzej Krol
    • 3
  • Axel Wismüller
    • 1
  1. 1.Departments of Imaging Sciences and Biomedical EngineeringUniversity of RochesterRochesterUSA
  2. 2.Department of RadiologyLudwig Maximilians UniversitätMünchenGermany
  3. 3.Department of RadiologySUNY Upstate Medical UniversitySyracuseUSA

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